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Description
Online product ratings offer consumers information about products. In this dissertation, I explore how the design of the rating system impacts consumers’ sharing behavior and how different players are affected by rating mechanisms. The first two chapters investigate how consumers choose to share their experiences of different attributes, how their preferences are reflected in numerical ratings and textual reviews, whether and how multi-dimensional rating systems affect consumer satisfaction through product ratings, and whether and how multi-dimensional rating systems affect the interplay between numerical ratings and textual reviews. The identification strategy of the observational study hinges on a natural experiment on TripAdvisor when the website reengineered its rating system from single-dimensional to multi-dimensional in January 2009. Rating data on the same set of restaurants from Yelp, were used to identify the causal effect using a difference-in-difference approach. Text mining skills were deployed to identify potential topics from textual reviews when consumers didn’t provide dimensional ratings in both SD and MD systems. Results show that ratings in a single-dimensional rating system have a downward trend and a higher dispersion, whereas ratings in a multi-dimensional rating system are significantly higher and convergent. Textual reviews in MDR are in greater width and depth than textual reviews in SDR. The third chapter tries to uncover how the introduction of monetary incentives would influence different players in the online e-commerce market in the short term and in the long run. These three studies together contribute to the understanding of rating system/mechanism designs and different players in the online market.
ContributorsLiu, Ying (Author) / Chen, Pei-Yu (Thesis advisor) / Hong, Yili (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2018
Description疫情期间，人们与社区发生了高频链接。流调、核酸、求助，社区工作者无时不刻出现在人们的生活中。社区作为中国基层治理的基础单元，政府需要上而下到社区执行防控工作，百姓则自下而上需要社区提供服务和帮助、向上反映各类居民诉求。伴随着中国的城镇化进程，全中国有9万个城市社区、400万社区工作者，社区参与主体从居委会、物业到民非组织，不断地进化。在人们对未来生活需求不断提高的时侯，社区服务提供者协同社区各主体，实现数字社区可持续发展？本研究围绕“社区服务提供者如何在后疫情时代构建可持续的社区协同机制？”这一研究问题，首先，对数字社区可持续发展和数字社区协同机制相关研究进行梳理和回顾；第二，将以数字社区建设和协同治理为出发点，以七彩集团为研究样本，分析其实际运营的滨江缤纷未来社区和萧山瓜沥未来社区案例，归纳总结其协同治理的具体措施；第三，结合理论规范分析，提出“数字社区协同机制-协同绩效”的理论框架和作用边界；第四，运用问卷调查结合因子分析的方法，对这些具体措施能够实际提高企业绩效进行问卷发放和数据验证。
本研究得到以下三个主要结论：（1）数字社区中可持续性发展协同治理机制是社区运营主体利用数字化技术对社区内参与提供和使用服务的治理活动进行约束、激励、引导和管理的一系列制度安排，同时包括政策治理、社区文化和市场共建三种不同作用机制。（2）数字社区中，治理政策机制仅在社团主体中作用显著；社区文化除了在社区物业中作用有限，在其他所有主体中均发挥重要作用；市场共建则是在社团、物业和居民业主中发挥作用。（3）协同机制通过政策治理、社区文化、市场共建影响社区内不同主体的感知和行为，而数字技术作用一种新兴的支撑性技术能够对上述作用产生不同的增强作用，进而促进协同绩效提升。
本研究通过聚焦于社区运营六方主体的角色分工、各自诉求，进一步讨论如何应用最新数字经济和技术来找到可持续发展的协同机制，为后疫情时代中国社区的良性发展找到解决方案。
ContributorsXu, Xiaowei (Author) / Shao, Benjamin (Thesis advisor) / Zhang, Anmin (Thesis advisor) / Gu, Bin (Committee member) / Hong, Yili (Committee member) / Arizona State University (Publisher)
Created2023
Description近几年来，科技创新已经成为高新技术企业发展的重要动力源泉，如何提高企业的创新绩效，增强企业的竞争力，一直以来都是广大学者讨论的热点。高管是公司的核心，也是公司战略决策机构的主体，同时还是企业与内外部环境进行联系的重要纽带。高管的学习力会影响到高管群体职能的发挥，更会影响到企业的决策，这对企业的创新绩效有着母庸置疑的影响。目前，对于高管个人学习力的研究文献较少，关于高管学习力对创新绩效的作用机制的研究更是匮乏。高管学习力与创新绩效是怎么样的关系，哪些要素影响到了这两者的关系，都是需要进行深入挖掘的问题。本文深入探究了高管学习力与创新绩效的关系，并详细分析了高管学习力对创新绩效的影响路径和影响机制。主要的研究内容如下：
本文根据基础理论，分析了学习的转化机制与企业动态能力之间的关系，建立起本文的研究模型。利用陈国权的个人学习力模型，将高管学习力分为9个维度，同时确定组织学习为中介变量，调节变量确定为主动性人格与环境动态性。基于研究模型和现有的文献内容，本文提出了研究假设。而后通过问卷的方式收集到相关数据，通过对数据进行信度分析、效度分析和相关性分析后，确定了数据的可靠性。最后，通过描述性分析，回归分析的方法对研究假设进行了验证。
最终本文得出了以下结论：
（1）高管学习力能够正向影响到创新绩效。（2）高管学习力能够正向影响到组织学习。（3）组织学习能够正向影响到创新绩效。（4）在高管学习力与创新绩效的关系中，组织学习起到中介作用。（5）在高管学习力与组织学习的关系中，主动性人格起到正向调节作用。（6）在组织学习与创新绩效的关系中，环境动态性起到正向调节作用。
基于上述研究成果，本文对高管学习力和创新绩效之间的关系探究做了一次有益的尝试，为广大的企业和学者提供了一个崭新的思路和视角。同时也丰富了关于高管学习力和企业创新绩效的相关研究内容。
ContributorsTu, Yihua (Author) / Zhu, David (Thesis advisor) / Yan, Hong (Thesis advisor) / Hong, Yili (Committee member) / Arizona State University (Publisher)
Created2023
Description
Two-sided online platforms are typically plagued by hidden information (adverse selection) and hidden actions (moral hazard), limiting market efficiency. Under the context of the increasingly popular online labor contracting platforms, this dissertation investigates whether and how IT-enabled monitoring systems can mitigate moral hazard and reshape the labor demand and supply by providing detailed information about workers’ effort. In the first chapter, I propose and demonstrate that monitoring records can substitute for reputation signals such that they attract more qualified inexperienced workers to enter the marketplace. Specifically, only the effort-related reputation information is substituted by monitoring but the capability-related reputation information. In line with this, monitoring can lower the entry barrier for inexperienced workers on platforms. In the second chapter, I investigate if there is home bias for local workers when employers make the hiring decisions. I further show the existence of home bias from employers and it is primarily driven by statistical inference instead of personal “taste”. In the last chapter, I examine if females tend to have a stronger avoidance of monitoring than males. With the combination of the observational data and experimental data, I find that there is a gender difference in avoidance of monitoring and the introduction of the monitoring system increases the gender wage gap due to genders differences in such willingness-to-pay for the avoidance of monitoring. These three studies jointly contribute to the literature on the online platforms, gig economy and agency theory by elucidating the critical role of IT-enabled monitoring.
ContributorsLiang, Chen, Ph.D (Author) / Gu, Bin (Thesis advisor) / Hong, Yili (Thesis advisor) / Chen, Peiyu (Committee member) / Arizona State University (Publisher)
Created2019
Description
Degradation process, as a course of progressive deterioration, commonly exists on many engineering systems. Since most failure mechanisms of these systems can be traced to the underlying degradation process, utilizing degradation data for reliability prediction is much needed. In industries, accelerated degradation tests (ADTs) are widely used to obtain timely reliability information of the system under test. This dissertation develops methodologies for the ADT data modeling and analysis.
In the first part of this dissertation, ADT is introduced along with three major challenges in the ADT data analysis – modeling framework, inference method, and the need of analyzing multi-dimensional processes. To overcome these challenges, in the second part, a hierarchical approach, that leads to a nonlinear mixed-effects regression model, to modeling a univariate degradation process is developed. With this modeling framework, the issues of ignoring uncertainties in both data analysis and lifetime prediction, as presented by an International Standard Organization (ISO) standard, are resolved. In the third part, an approach to modeling a bivariate degradation process is addressed. It is developed using the copula theory that brings the benefits of both model flexibility and inference convenience. This approach is provided with an efficient Bayesian method for reliability evaluation. In the last part, an extension to a multivariate modeling framework is developed. Three fundamental copula classes are applied to model the complex dependence structure among correlated degradation processes. The advantages of the proposed modeling framework and the effect of ignoring tail dependence are demonstrated through simulation studies. The applications of the copula-based multivariate degradation models on both system reliability evaluation and remaining useful life prediction are provided.
In summary, this dissertation studies and explores the use of statistical methods in analyzing ADT data. All proposed methodologies are demonstrated by case studies.
In the first part of this dissertation, ADT is introduced along with three major challenges in the ADT data analysis – modeling framework, inference method, and the need of analyzing multi-dimensional processes. To overcome these challenges, in the second part, a hierarchical approach, that leads to a nonlinear mixed-effects regression model, to modeling a univariate degradation process is developed. With this modeling framework, the issues of ignoring uncertainties in both data analysis and lifetime prediction, as presented by an International Standard Organization (ISO) standard, are resolved. In the third part, an approach to modeling a bivariate degradation process is addressed. It is developed using the copula theory that brings the benefits of both model flexibility and inference convenience. This approach is provided with an efficient Bayesian method for reliability evaluation. In the last part, an extension to a multivariate modeling framework is developed. Three fundamental copula classes are applied to model the complex dependence structure among correlated degradation processes. The advantages of the proposed modeling framework and the effect of ignoring tail dependence are demonstrated through simulation studies. The applications of the copula-based multivariate degradation models on both system reliability evaluation and remaining useful life prediction are provided.
In summary, this dissertation studies and explores the use of statistical methods in analyzing ADT data. All proposed methodologies are demonstrated by case studies.
ContributorsFANG, GUANQI (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Ju, Feng (Committee member) / Hong, Yili (Committee member) / Arizona State University (Publisher)
Created2020
Description随着中国居民消费占GDP比例的提升，人均GDP的增长，银行等贷款机构对个人和零售业务的长期发展，中国金融机构的个人贷款不良资产规模发生了很大的变化。居民个人对外负债主要是以债权方式体现。基于债权的一致性，对于借贷人的个人外部负债缺少特定的强制性的偿贷顺序安排，偿贷行为也不会受到法律框架的强制约束，导致借贷人在偿贷能力不足时，个体的主观意愿对于偿贷行为结果的影响显著。
既往的个人贷款不良资产管理和服务模式，均参照企业贷款不良资产管理和服务模式，体现出在标的资产的产品特点、信贷主体差异、法律完备性、对公共基础服务支撑要求等方面存在显著的不同。原有针对企业贷款不良资产的管理和服务模式在适应个人贷款不良资产管理和服务时，也就需要采用不同的方式和策略，所以，优化、提升对于借贷人的管理和服务模式就存在必要的调整和优化空间。
由于借贷人的自然人属性，区别于企业的法人属性，其生命周期自然存续期间，偿贷能力存在修复的可能，外部征信环境的改善，也会对个人贷款不良资产的产生影响。现有的个人贷款不良资产的管理和服务模式也需要做出必要的调整过和安排。
21世纪前20年，互联网/通讯/IT技术发展迅速，AI、BigData、Blockchain等技术逐渐成熟，对厘清个人贷款不良资产偿贷机制提供了必要的基础数据。在此基础上，运用日趋完备的信息不对称和行为决策等理论工具，对既有对个人贷款不良资产管理和服务模式做出优化和调整就存在可能性。
本文基于P2P个人贷款不良资产管理和服务过程中形成的数据，选取金额回款率和失联事件发生率来计量借款人的行为决策结果，通过对这两个指标在个人贷款不良资产管理和服务中呈现的规律进行分析，初步厘清了个人贷款不良资产的偿贷过程中的行为决策机制，在既有框架基础上，对个人贷款不良资产管理和服务中的资产定价优化、资产交易模式、管理和服务机构评价、不良资产策略管理策略、催收服务策略等提供提供了有益的补充。
ContributorsGuo, Dagang (Author) / Huang, Xiaochuan (Thesis advisor) / Chang, Chun (Thesis advisor) / Zhu, Ning (Committee member) / Hong, Yili (Committee member) / Arizona State University (Publisher)
Created2021
Description
Various activities move online in the era of the digital economy. Platform design and policy can heavily affect online user activities and result in many expected and unexpected consequences. In this dissertation, I conduct empirical studies on three types of online platforms to investigate the influence of their platform policy on their user engagement and associated outcomes. Specifically, in Study 1, I focus on goal-directed platforms and study how the introduction of the mobile channel affects users’ goal pursuit engagement and persistence. In Study 2, I focus on social media and online communities. I study the introduction of machine-powered platform regulation and its impacts on volunteer moderators’ engagement. In Study 3, I focus on online political discourse forums and examine the role of identity declaration in user participation and polarization in the subsequent political discourse. Overall, my results highlight how various platform policies shape user behavior. Implications on multi-channel adoption, human-machine collaborative platform governance, and online political polarization research are discussed.
ContributorsHe, Qinglai (Author) / Santanam, Raghu (Thesis advisor) / Hong, Yili (Thesis advisor) / Burtch, Gordon (Committee member) / Arizona State University (Publisher)
Created2021
Description
In the past decade, online shopping mode has been recognized and accepted by more and
more people. Over 200 million people were online shoppers in the United States. Convenient,
options, and better prices compared to traditional shopping mode attract more people to choose
the products online. Consumer’s feedback presented as online reviews on products after the
purchase has become one of the most important factors influencing whether other consumers will
purchase products. For merchants, by studying the behavioral differences of these online
consumers when evaluating products, they can help them to understand product characteristics
and their customers to improve online marketing strategies. This article explores the differences
in the types of utilitarian and hedonic products and behavioral changes in customer opinions,
which involves 22 different categories of products from Amazon.com and customer reviews for
analysis through a variety of technical and research methods.
more people. Over 200 million people were online shoppers in the United States. Convenient,
options, and better prices compared to traditional shopping mode attract more people to choose
the products online. Consumer’s feedback presented as online reviews on products after the
purchase has become one of the most important factors influencing whether other consumers will
purchase products. For merchants, by studying the behavioral differences of these online
consumers when evaluating products, they can help them to understand product characteristics
and their customers to improve online marketing strategies. This article explores the differences
in the types of utilitarian and hedonic products and behavioral changes in customer opinions,
which involves 22 different categories of products from Amazon.com and customer reviews for
analysis through a variety of technical and research methods.
ContributorsXu, Jiawei (Author) / Huang, Ni (Thesis director) / Hong, Yili (Committee member) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05